Graph Regression
88 papers with code • 12 benchmarks • 17 datasets
The regression task is similar to graph classification but using different loss function and performance metric.
Benchmarks
These leaderboards are used to track progress in Graph Regression
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Libraries
Use these libraries to find Graph Regression models and implementationsDatasets
ZINC
MoleculeNet
Long Range Graph Benchmark (LRGB)
OGB-LSC
Tox21
PCQM4Mv2-LSC
ESOL (Estimated SOLubility)
$O_2$Perm
GlassTemp
Cylinder in Crossflow
Latest papers
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
shamim-hussain/egt_pytorch
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We also obtain SOTA results on QM9, MOLPCBA, and LIT-PCBA molecular property prediction benchmarks via transfer learning.
Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings
nur-ag/elene
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We present a novel edge-level ego-network encoding for learning on graphs that can boost Message Passing Graph Neural Networks (MP-GNNs) by providing additional node and edge features or extending message-passing formats.
Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding
pku-ml/laplaciancanonization
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NeurIPS 2023
However, from a theoretical perspective, the universal expressive power of spectral embedding comes at the price of losing two important invariance properties of graphs, sign and basis invariance, which also limits its effectiveness on graph data.
Infinite Width Graph Neural Networks for Node Regression/ Classification
yCobanoglu/infinite-width-gnns
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This work analyzes Graph Neural Networks, a generalization of Fully-Connected Deep Neural Nets on Graph structured data, when their width, that is the number of nodes in each fullyconnected layer is increasing to infinity.
Graph-level Representation Learning with Joint-Embedding Predictive Architectures
Joint-Embedding Predictive Architectures (JEPAs) have recently emerged as a novel and powerful technique for self-supervised representation learning.
Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark
toenshoff/lrgb
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The recent Long-Range Graph Benchmark (LRGB, Dwivedi et al. 2022) introduced a set of graph learning tasks strongly dependent on long-range interaction between vertices.
Modeling Edge Features with Deep Bayesian Graph Networks
We propose an extension of the Contextual Graph Markov Model, a deep and probabilistic machine learning model for graphs, to model the distribution of edge features.
DeSCo: Towards Generalizable and Scalable Deep Subgraph Counting
fuvty/DeSCo
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We introduce DeSCo, a scalable neural deep subgraph counting pipeline, designed to accurately predict both the count and occurrence position of queries on target graphs post single training.
Towards Temporal Edge Regression: A Case Study on Agriculture Trade Between Nations
scylj1/gnn_edge_regression
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In this paper, we explore the application of GNNs to edge regression tasks in both static and dynamic settings, focusing on predicting food and agriculture trade values between nations.
Substructure Aware Graph Neural Networks
BlackHalo-Drake/SAGNN-Substructure-Aware-Graph-Neural-Networks
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Proceedings of the AAAI Conference on Artificial Intelligence 2023
Despite the great achievements of Graph Neural Networks (GNNs) in graph learning, conventional GNNs struggle to break through the upper limit of the expressiveness of first-order Weisfeiler-Leman graph isomorphism test algorithm (1-WL) due to the consistency of the propagation paradigm of GNNs with the 1-WL. Based on the fact that it is easier to distinguish the original graph through subgraphs, we propose a novel framework neural network framework called Substructure Aware Graph Neural Networks (SAGNN) to address these issues.
